
import pandas as pd
import numpy as np

import seaborn as sn
import plotly.express as px
import plotly.offline as pyo
import matplotlib.pyplot as plt

import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns',None)

import os


df = pd.read_csv('Chennai houseing sale.csv')
df.head()


df.shape

(7109, 22)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7109 entries, 0 to 7108
Data columns (total 22 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   PRT_ID         7109 non-null   object 
 1   AREA           7109 non-null   object 
 2   INT_SQFT       7109 non-null   int64  
 3   DATE_SALE      7109 non-null   object 
 4   DIST_MAINROAD  7109 non-null   int64  
 5   N_BEDROOM      7108 non-null   float64
 6   N_BATHROOM     7104 non-null   float64
 7   N_ROOM         7109 non-null   int64  
 8   SALE_COND      7109 non-null   object 
 9   PARK_FACIL     7109 non-null   object 
 10  DATE_BUILD     7109 non-null   object 
 11  BUILDTYPE      7109 non-null   object 
 12  UTILITY_AVAIL  7109 non-null   object 
 13  STREET         7109 non-null   object 
 14  MZZONE         7109 non-null   object 
 15  QS_ROOMS       7109 non-null   float64
 16  QS_BATHROOM    7109 non-null   float64
 17  QS_BEDROOM     7109 non-null   float64
 18  QS_OVERALL     7061 non-null   float64
 19  REG_FEE        7109 non-null   int64  
 20  COMMIS         7109 non-null   int64  
 21  SALES_PRICE    7109 non-null   int64  
dtypes: float64(6), int64(6), object(10)
memory usage: 1.2+ MB


df.isnull().sum()

PRT_ID            0
AREA              0
INT_SQFT          0
DATE_SALE         0
DIST_MAINROAD     0
N_BEDROOM         1
N_BATHROOM        5
N_ROOM            0
SALE_COND         0
PARK_FACIL        0
DATE_BUILD        0
BUILDTYPE         0
UTILITY_AVAIL     0
STREET            0
MZZONE            0
QS_ROOMS          0
QS_BATHROOM       0
QS_BEDROOM        0
QS_OVERALL       48
REG_FEE           0
COMMIS            0
SALES_PRICE       0
dtype: int64


from sklearn.impute import SimpleImputer

imputer=SimpleImputer(missing_values=np.nan,strategy='mean',fill_value=None)

df['N_BEDROOM'] = imputer.fit_transform(df[['N_BEDROOM']])
df['N_BATHROOM'] = imputer.fit_transform(df[['N_BATHROOM']])
df['QS_OVERALL'] = imputer.fit_transform(df[['QS_OVERALL']])


for col in df.columns:
    if df[col].dtype=='object':
        print()
        print(col)
        print(df[col].unique())

PRT_ID
['P03210' 'P09411' 'P01812' ... 'P09594' 'P06508' 'P09794']

AREA
['Karapakkam' 'Anna Nagar' 'Adyar' 'Velachery' 'Chrompet' 'KK Nagar'
 'TNagar' 'T Nagar' 'Chrompt' 'Chrmpet' 'Karapakam' 'Ana Nagar' 'Chormpet'
 'Adyr' 'Velchery' 'Ann Nagar' 'KKNagar']

DATE_SALE
['04-05-2011' '19-12-2006' '04-02-2012' ... '28-03-2014' '25-08-2006'
 '13-07-2005']

SALE_COND
['AbNormal' 'Family' 'Partial' 'AdjLand' 'Normal Sale' 'Ab Normal'
 'Partiall' 'Adj Land' 'PartiaLl']

PARK_FACIL
['Yes' 'No' 'Noo']

DATE_BUILD
['15-05-1967' '22-12-1995' '09-02-1992' ... '01-09-1978' '11-08-1977'
 '24-07-1961']

BUILDTYPE
['Commercial' 'Others' 'Other' 'House' 'Comercial']

UTILITY_AVAIL
['AllPub' 'ELO' 'NoSewr ' 'NoSeWa' 'All Pub']

STREET
['Paved' 'Gravel' 'No Access' 'Pavd' 'NoAccess']

MZZONE
['A' 'RH' 'RL' 'I' 'C' 'RM']


df.AREA.replace(['Ana Nagar','Ann Nagar'],'Anna Nagar',inplace=True)
df.AREA.replace('Karapakkam','Karapakam',inplace=True)
df.AREA.replace(['Chrompt','Chrmpet','Chormpet'],'Chrompet',inplace=True)
df.AREA.replace('KKNagar','KK Nagar',inplace=True)
df.AREA.replace('TNagar','T Nagar',inplace=True)
df.AREA.replace('Adyr','Adyar',inplace=True)
df.AREA.replace('Velchery','Velachery',inplace=True)

df.BUILDTYPE.replace('Comercial','Commercial',inplace=True)
df.BUILDTYPE.replace('Other','Others',inplace=True)

df.UTILITY_AVAIL.replace('AllPub','All Pub',inplace=True)
df.UTILITY_AVAIL.replace(['NoSewr','NoSewr'],'NoSeWa',inplace=True)

df.SALE_COND.replace('Ab Normal','AbNormal',inplace=True)
df.SALE_COND.replace(['PartiaLl','Partiall'],'Partial',inplace=True)
df.SALE_COND.replace('Adj Land','AdjLand',inplace=True)

df.PARK_FACIL.replace('Noo','No',inplace=True)

df.STREET.replace('Pavd','Paved',inplace=True)
df.STREET.replace('NoAccess','No Access',inplace=True)


df.QS_ROOMS=df.QS_ROOMS.astype(int)
df.QS_BATHROOM=df.QS_BATHROOM.astype(int)
df.QS_BEDROOM=df.QS_BEDROOM.astype(int)
df.N_BEDROOM=df.N_BEDROOM.astype(int)
df.N_BATHROOM=df.N_BATHROOM.astype(int)


df.head()


df.describe()


df.AREA.value_counts()

Chrompet      1702
Karapakam     1366
KK Nagar       997
Velachery      981
Anna Nagar     788
Adyar          774
T Nagar        501
Name: AREA, dtype: int64


df.groupby('AREA',as_index=False).count()


fig = px.pie(df.groupby('AREA',as_index=False).count(),
       names='AREA',
       values='PRT_ID',
       #labels={'PRT_ID':'Count'},
       template='plotly_dark',
       color_discrete_sequence=px.colors.sequential.Plasma,
       hole=.5,
       title='<b> Houses Count in different Areas of Chennai<b>'
      )

fig.show()
pyo.plot(fig, filename = 'Houses Count in different Areas of Chennai.html', auto_open = False)

'Houses Count in different Areas of Chennai.html'


fig = px.box(df,
       x='AREA',
       y='SALES_PRICE',
       color='AREA',
       template='plotly_dark',
       title='<b> Sales Price of Houses in different Areas')

fig.show()
pyo.plot(fig, filename = 'Sales Price of Houses in different Areas.html', auto_open = False)

'Sales Price of Houses in different Areas.html'


fig = px.scatter(df,
           x='INT_SQFT',
           y='SALES_PRICE',
           color='AREA',
           size='INT_SQFT',
           labels={'INT_SQFT':'SQFT'},
           template='plotly_dark',
           title='<b> SQFT Versus SALES PRICE of Houses in Different Areas')

fig.show()
pyo.plot(fig, filename = 'SQFT Versus SALES PRICE of Houses in Different Areas.html', auto_open = False)

'SQFT Versus SALES PRICE of Houses in Different Areas.html'


fig = px.scatter(df,
           x='DIST_MAINROAD',
           y='SALES_PRICE',
           color='AREA',
           size='SALES_PRICE',
           template='plotly_dark',
           title='<b> MAINROAD DISTANCE Vs SALES_PRICE')

fig.show()
pyo.plot(fig, filename = 'MAINROAD DISTANCE Vs SALES_PRICE.html', auto_open = False)

'MAINROAD DISTANCE Vs SALES_PRICE.html'


fig =px.scatter(df,
               x='DATE_BUILD',
               y='SALES_PRICE',
               color='AREA',
               size='SALES_PRICE',
               template='plotly_dark',
               title='<b> Build Date Vs Sales Price')
fig.show()
pyo.plot(fig, filename = 'Build Date Vs Sales Price.html', auto_open = False)

fig=px.scatter(df,
               x='DATE_SALE',
               y='SALES_PRICE',
               color='AREA',
               size='SALES_PRICE',
               template='plotly_dark',
               title='<b> Sale Date Vs Sales Price')
fig.show()
pyo.plot(fig, filename = 'Sale Date Vs Sales Price.html', auto_open = False)

'Sale Date Vs Sales Price.html'


fig = px.box(df,
       x='N_ROOM',
       y='SALES_PRICE',
       color='AREA',
       template='plotly_dark',
       title='<b>Total Rooms Versus Sales Price of Houses in Different Areas')

fig.show()
pyo.plot(fig, filename = 'Total Rooms Versus Sales Price of Houses in Different Areas.html', auto_open = False)

'Total Rooms Versus Sales Price of Houses in Different Areas.html'


fig = px.box(df,
                 x='N_BEDROOM',
                 #y='AREA',
                 y='SALES_PRICE',
                 color = 'AREA',
                 template='plotly_dark',
                 title='<b>Total BedRooms Versus Sales Price of Houses in Different Areas')

fig.show()
pyo.plot(fig, filename = 'Total BedRooms Versus Sales Price of Houses in Different Areas.html', auto_open = False)

'Total BedRooms Versus Sales Price of Houses in Different Areas.html'


fig = px.box(df,
              x='N_BATHROOM',
              y='SALES_PRICE',
              color='AREA',
              template='plotly_dark',
              title='<b>Total BathRooms Versus Sales Price of Houses in Different Areas')

fig.show()
pyo.plot(fig, filename = 'Total BedRooms Versus Sales Price of Houses in Different Areas.html', auto_open = False)

'Total BedRooms Versus Sales Price of Houses in Different Areas.html'


fig = px.box(df,
       x='SALE_COND',
       y='SALES_PRICE',
       color='AREA',
       template='plotly_dark',
       title='<b>Sale Condition Versus Sales Price of Houses in Different Areas')

fig.show()
pyo.plot(fig, filename = 'Sale Condition Versus Sales Price of Houses in Different Areas.html', auto_open = False)

'Sale Condition Versus Sales Price of Houses in Different Areas.html'


fig = px.box(df,
       x='PARK_FACIL',
       y='SALES_PRICE',
       color='AREA',
       template='plotly_dark',
       title='<b>Parking Facility Versus Sales Price of Houses in Different Areas')


fig.show()
pyo.plot(fig, filename = 'Parking Facility Versus Sales Price of Houses in Different Areas.html', auto_open = False)

'Parking Facility Versus Sales Price of Houses in Different Areas.html'


fig = px.box(df,
       x='BUILDTYPE',
       y='SALES_PRICE',
       color='AREA',
       template='plotly_dark',
       title='<b>Build Type Versus Sales Price of Houses in Different Areas')

fig.show()
pyo.plot(fig, filename = 'Build Type Versus Sales Price of Houses in Different Areas.html', auto_open = False)

'Build Type Versus Sales Price of Houses in Different Areas.html'


fig = px.box(df,
       x='STREET',
       y='SALES_PRICE',
       color='AREA',
       template='plotly_dark',
       title='<b> Street Versus Sales Price of Houses in Different Areas')

fig.show()
pyo.plot(fig, filename = 'Street Versus Sales Price of Houses in Different Areas.html', auto_open = False)

'Street Versus Sales Price of Houses in Different Areas.html'


df.corr()


plt.figure(figsize=(12,6))
sn.heatmap(df.corr(),annot=True,cmap='CMRmap')
plt.title('Heatmap of the Data')
plt.show()


# Creating a Back up File
df1=df.copy()


from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()


for col in df.columns:
    if df[col].dtype=='object':
        df[col]=le.fit_transform(df[col])


df.head()


x=df.drop('SALES_PRICE',axis=1)
y=df['SALES_PRICE']


from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25,random_state=42)
x_train.shape,x_test.shape,y_train.shape,y_test.shape

((5331, 21), (1778, 21), (5331,), (1778,))


from sklearn.preprocessing import MinMaxScaler
mmscaler=MinMaxScaler(feature_range=(0,1))
ms = mmscaler.fit(x_train)

x_train = ms.transform(x_train)
x_test = ms.transform(x_test)
x_train = pd.DataFrame(x_train)
x_test = pd.DataFrame(x_test)


a = {'Model Name':[], 
     'Mean_Absolute_Error_MAE':[] ,
     'Adj_R_Square':[] ,
     'Root_Mean_Squared_Error_RMSE':[] ,
     'Mean_Absolute_Percentage_Error_MAPE':[] ,
     'Mean_Squared_Error_MSE':[] ,
     'Root_Mean_Squared_Log_Error_RMSLE':[] ,
     'R2_score':[]}

Results=pd.DataFrame(a)
Results.head()


# Import the library
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR
import xgboost as xgb
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import GradientBoostingRegressor


lg = LinearRegression()
dt = DecisionTreeRegressor()
rf = RandomForestRegressor()
SVR = SVR()
xgbr = xgb.XGBRegressor()
KNN = KNeighborsRegressor(n_neighbors=5)
ETR = ExtraTreesRegressor()

Model = [lg, dt, rf, SVR,KNN, ETR, xgbr]


model_GBR = GradientBoostingRegressor(loss='ls', learning_rate=0.1, n_estimators=100, subsample=1.0,
                                     criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1,
                                     min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0,
                                     init=None, random_state=None, max_features=None,
                                     alpha=0.9, verbose=0, max_leaf_nodes=None, warm_start=False,
                                     validation_fraction=0.1, n_iter_no_change=None, tol=0.0001, ccp_alpha=0.0)


# Evalution matrix for all the algorithms

for model in Model:
    
    # Fit the model with train data
    model.fit(x_train, y_train)
    
    # Predict the model with test data
    y_pred = model.predict(x_test)
    
    # Print the model name
    print('Model Name: ', model)
    
    # Evaluation metrics for Regression analysis
    from sklearn import metrics

    print('Mean Absolute Error (MAE):', round(metrics.mean_absolute_error(y_test, y_pred),3))  
    print('Mean Squared Error (MSE):', round(metrics.mean_squared_error(y_test, y_pred),3))  
    print('Root Mean Squared Error (RMSE):', round(np.sqrt(metrics.mean_squared_error(y_test, y_pred)),3))
    print('R2_score:', round(metrics.r2_score(y_test, y_pred),6))
    print('Root Mean Squared Log Error (RMSLE):', round(np.log(np.sqrt(metrics.mean_squared_error(y_test, y_pred))),3))
    
    # Define the function to calculate the MAPE - Mean Absolute Percentage Error
    def MAPE (y_test, y_pred):
        y_test, y_pred = np.array(y_test), np.array(y_pred)
        return np.mean(np.abs((y_test - y_pred) / y_test)) * 100
    
    # Evaluation of MAPE 
    result = MAPE(y_test, y_pred)
    print('Mean Absolute Percentage Error (MAPE):', round(result, 2), '%')
    
    # Calculate Adjusted R squared values 
    r_squared = round(metrics.r2_score(y_test, y_pred),6)
    adjusted_r_squared = round(1 - (1-r_squared)*(len(y)-1)/(len(y)-x.shape[1]-1),6)
    print('Adj R Square: ', adjusted_r_squared)
    print('******************************************************************************************************************')
    #-------------------------------------------------------------------------------------------
    new_row = {'Model Name' : model,
               'Mean_Absolute_Error_MAE' : metrics.mean_absolute_error(y_test, y_pred),
               'Adj_R_Square' : adjusted_r_squared,
               'Root_Mean_Squared_Error_RMSE' : np.sqrt(metrics.mean_squared_error(y_test, y_pred)),
               'Mean_Absolute_Percentage_Error_MAPE' : result,
               'Mean_Squared_Error_MSE' : metrics.mean_squared_error(y_test, y_pred),
               'Root_Mean_Squared_Log_Error_RMSLE': np.log(np.sqrt(metrics.mean_squared_error(y_test, y_pred))),
               'R2_score' : metrics.r2_score(y_test, y_pred)}
    Results = Results.append(new_row, ignore_index=True)
    print()

Model Name:  LinearRegression()
Mean Absolute Error (MAE): 1022554.549
Mean Squared Error (MSE): 1663603131167.479
Root Mean Squared Error (RMSE): 1289807.401
R2_score: 0.875211
Root Mean Squared Log Error (RMSLE): 14.07
Mean Absolute Percentage Error (MAPE): 10.0 %
Adj R Square:  0.874841
******************************************************************************************************************

Model Name:  DecisionTreeRegressor()
Mean Absolute Error (MAE): 766160.228
Mean Squared Error (MSE): 1083848977254.26
Root Mean Squared Error (RMSE): 1041080.678
R2_score: 0.918699
Root Mean Squared Log Error (RMSLE): 13.856
Mean Absolute Percentage Error (MAPE): 7.61 %
Adj R Square:  0.918458
******************************************************************************************************************

Model Name:  RandomForestRegressor()
Mean Absolute Error (MAE): 529879.249
Mean Squared Error (MSE): 451763452981.996
Root Mean Squared Error (RMSE): 672133.508
R2_score: 0.966113
Root Mean Squared Log Error (RMSLE): 13.418
Mean Absolute Percentage Error (MAPE): 5.39 %
Adj R Square:  0.966013
******************************************************************************************************************

Model Name:  SVR()
Mean Absolute Error (MAE): 2830943.966
Mean Squared Error (MSE): 13621656915965.639
Root Mean Squared Error (RMSE): 3690752.893
R2_score: -0.02178
Root Mean Squared Log Error (RMSLE): 15.121
Mean Absolute Percentage Error (MAPE): 28.67 %
Adj R Square:  -0.024808
******************************************************************************************************************

Model Name:  KNeighborsRegressor()
Mean Absolute Error (MAE): 1260718.96
Mean Squared Error (MSE): 2848745691648.293
Root Mean Squared Error (RMSE): 1687822.767
R2_score: 0.786312
Root Mean Squared Log Error (RMSLE): 14.339
Mean Absolute Percentage Error (MAPE): 11.92 %
Adj R Square:  0.785679
******************************************************************************************************************

Model Name:  ExtraTreesRegressor()
Mean Absolute Error (MAE): 369291.913
Mean Squared Error (MSE): 217005195335.877
Root Mean Squared Error (RMSE): 465838.164
R2_score: 0.983722
Root Mean Squared Log Error (RMSLE): 13.052
Mean Absolute Percentage Error (MAPE): 3.82 %
Adj R Square:  0.983674
******************************************************************************************************************

Model Name:  XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,
             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
             early_stopping_rounds=None, enable_categorical=False,
             eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
             importance_type=None, interaction_constraints='',
             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
             missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=0,
             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,
             reg_lambda=1, ...)
Mean Absolute Error (MAE): 357348.064
Mean Squared Error (MSE): 213624495167.217
Root Mean Squared Error (RMSE): 462195.3
R2_score: 0.983976
Root Mean Squared Log Error (RMSLE): 13.044
Mean Absolute Percentage Error (MAPE): 3.57 %
Adj R Square:  0.983929
******************************************************************************************************************


Results


# Training the Model
xgbr.fit(x_train, y_train) 

# Predict the model with test data
y_pred_xgb = xgbr.predict(x_test)


# Training the Model
ETR.fit(x_train, y_train) 

# Predict the model with test data
y_pred_ETR = ETR.predict(x_test)


output= pd.DataFrame({'Price_actual':y_test, 'Price_pred (XGB)':y_pred_xgb, 'Price_pred (ETR)': y_pred_ETR})
result=df1.merge(output,left_index=True,right_index=True)

#result.head()
result[['PRT_ID','AREA','Price_actual','Price_pred (XGB)','Price_pred (ETR)']].sample(20)


fig = px.scatter(result,
                 x='Price_actual',
                 y='Price_pred (XGB)',
                 trendline='ols',
                 color = 'AREA',
                 template='plotly_dark',
                 title='<b> Actual Price  Vs  Predicted Price with XGBRegressor')
fig.show()

fig = px.scatter(result,
                 x='Price_actual',
                 y='Price_pred (ETR)',
                 trendline='ols',
                 color = 'AREA',
                 template='plotly_dark',
                 title='<b> Actual Price  Vs  Predicted Price with ExtraTreesRegressor')
fig.show()
pyo.plot(fig, filename = 'Actual Price  Vs  Predicted Price.html', auto_open = False)

'Actual Price  Vs  Predicted Price.html'

	INT_SQFT	DIST_MAINROAD	N_BEDROOM	N_BATHROOM	N_ROOM	QS_ROOMS	QS_BATHROOM	QS_BEDROOM	QS_OVERALL	REG_FEE	COMMIS	SALES_PRICE
count	7109.000000	7109.000000	7109.000000	7109.000000	7109.000000	7109.000000	7109.000000	7109.000000	7109.000000	7109.000000	7109.000000	7.109000e+03
mean	1382.073006	99.603179	1.636939	1.213110	3.688704	3.075538	3.069349	3.056126	3.503254	376938.330708	141005.726544	1.089491e+07
std	457.410902	57.403110	0.802881	0.409534	1.019099	0.872937	0.876024	0.871499	0.525440	143070.662010	78768.093718	3.768603e+06
min	500.000000	0.000000	1.000000	1.000000	2.000000	2.000000	2.000000	2.000000	2.000000	71177.000000	5055.000000	2.156875e+06
25%	993.000000	50.000000	1.000000	1.000000	3.000000	2.000000	2.000000	2.000000	3.130000	272406.000000	84219.000000	8.272100e+06
50%	1373.000000	99.000000	1.000000	1.000000	4.000000	3.000000	3.000000	3.000000	3.503254	349486.000000	127628.000000	1.033505e+07
75%	1744.000000	148.000000	2.000000	1.000000	4.000000	4.000000	4.000000	4.000000	3.880000	451562.000000	184506.000000	1.299390e+07
max	2500.000000	200.000000	4.000000	2.000000	6.000000	5.000000	5.000000	5.000000	4.970000	983922.000000	495405.000000	2.366734e+07

	INT_SQFT	DIST_MAINROAD	N_BEDROOM	N_BATHROOM	N_ROOM	QS_ROOMS	QS_BATHROOM	QS_BEDROOM	QS_OVERALL	REG_FEE	COMMIS	SALES_PRICE
INT_SQFT	1.000000	0.002022	0.786263	0.515290	0.951279	0.016146	-0.011463	0.005847	0.013989	0.657544	0.571076	0.612125
DIST_MAINROAD	0.002022	1.000000	-0.002723	0.001850	0.002301	0.013050	-0.021857	0.002757	-0.017255	0.011600	0.010994	0.018783
N_BEDROOM	0.786263	-0.002723	1.000000	0.755207	0.840209	0.011434	-0.008603	0.014449	0.014245	0.455380	0.430446	0.330987
N_BATHROOM	0.515290	0.001850	0.755207	1.000000	0.568541	0.010452	-0.011005	0.013390	0.007961	0.260244	0.256726	0.108834
N_ROOM	0.951279	0.002301	0.840209	0.568541	1.000000	0.013468	-0.009066	0.012072	0.015418	0.630932	0.533343	0.602760
QS_ROOMS	0.016146	0.013050	0.011434	0.010452	0.013468	1.000000	0.001796	0.009590	0.489271	0.014614	0.007072	0.016059
QS_BATHROOM	-0.011463	-0.021857	-0.008603	-0.011005	-0.009066	0.001796	1.000000	-0.011917	0.513585	-0.014777	-0.008397	-0.019469
QS_BEDROOM	0.005847	0.002757	0.014449	0.013390	0.012072	0.009590	-0.011917	1.000000	0.598154	0.018875	0.019376	0.015044
QS_OVERALL	0.013989	-0.017255	0.014245	0.007961	0.015418	0.489271	0.513585	0.598154	1.000000	0.022410	0.016946	0.020485
REG_FEE	0.657544	0.011600	0.455380	0.260244	0.630932	0.014614	-0.014777	0.018875	0.022410	1.000000	0.659903	0.878148
COMMIS	0.571076	0.010994	0.430446	0.256726	0.533343	0.007072	-0.008397	0.019376	0.016946	0.659903	1.000000	0.626275
SALES_PRICE	0.612125	0.018783	0.330987	0.108834	0.602760	0.016059	-0.019469	0.015044	0.020485	0.878148	0.626275	1.000000

	PRT_ID	AREA	INT_SQFT	DATE_SALE	DIST_MAINROAD	N_BEDROOM	N_BATHROOM	N_ROOM	SALE_COND	PARK_FACIL	DATE_BUILD	BUILDTYPE	UTILITY_AVAIL	STREET	MZZONE	QS_ROOMS	QS_BATHROOM	QS_BEDROOM	QS_OVERALL	REG_FEE	COMMIS	SALES_PRICE
0	2266	4	1004	310	131	1	1	3	0	1	2731	0	0	2	0	4	3	4	4.330	380000	144400	7600000
1	6664	1	1986	1745	26	2	1	5	0	0	4226	0	0	0	3	4	4	2	3.765	760122	304049	21717770
2	1270	0	909	288	70	1	1	3	0	1	1511	0	1	0	4	4	3	2	3.090	421094	92114	13159200
3	3755	6	1855	1117	14	3	2	5	2	0	3300	2	3	2	2	4	3	3	4.010	356321	77042	9630290
4	4393	4	1226	442	84	1	1	3	0	1	2406	2	0	0	1	3	2	4	3.290	237000	74063	7406250

	Model Name	Mean_Absolute_Error_MAE	Adj_R_Square	Root_Mean_Squared_Error_RMSE	Mean_Absolute_Percentage_Error_MAPE	Mean_Squared_Error_MSE	Root_Mean_Squared_Log_Error_RMSLE	R2_score
0	LinearRegression()	1.022555e+06	0.874841	1.289807e+06	10.004632	1.663603e+12	14.070003	0.875211
1	DecisionTreeRegressor()	7.661602e+05	0.918458	1.041081e+06	7.613756	1.083849e+12	13.855770	0.918699
2	(DecisionTreeRegressor(max_features=1.0, rando...	5.298792e+05	0.966013	6.721335e+05	5.385192	4.517635e+11	13.418212	0.966113
3	SVR()	2.830944e+06	-0.024808	3.690753e+06	28.670171	1.362166e+13	15.121341	-0.021780
4	KNeighborsRegressor()	1.260719e+06	0.785679	1.687823e+06	11.924981	2.848746e+12	14.338950	0.786312
5	(ExtraTreeRegressor(random_state=1390877790), ...	3.692919e+05	0.983674	4.658382e+05	3.816738	2.170052e+11	13.051594	0.983722
6	XGBRegressor(base_score=0.5, booster='gbtree',...	3.573481e+05	0.983929	4.621953e+05	3.570879	2.136245e+11	13.043743	0.983976

	PRT_ID	AREA	Price_actual	Price_pred (XGB)	Price_pred (ETR)
3289	P04715	Karapakam	8553000	8648174.0	8289827.50
4220	P06076	Velachery	13685190	13847931.0	13796077.40
149	P07490	KK Nagar	12472000	12543332.0	12593784.00
3431	P06582	Karapakam	7866250	8243390.0	7543856.00
380	P09981	Velachery	12350640	13170537.0	12647901.20
6903	P01512	T Nagar	17414970	15758530.0	17159051.00
3197	P06601	Karapakam	8372750	8335412.0	7981913.25
3822	P05119	KK Nagar	16814260	17245898.0	17006137.40
2310	P05308	Karapakam	8008250	7777017.0	7953017.50
6044	P09738	Velachery	14602040	14162924.0	15240464.80
4538	P06888	Anna Nagar	21008600	20199684.0	20767024.00
4836	P05101	Velachery	13160490	13836980.0	13360752.70
3718	P04468	Karapakam	7689500	6666313.0	6443398.75
3317	P00958	Chrompet	10847700	10876618.0	10985148.90
2885	P01370	Chrompet	9190000	9059926.0	8989433.50
1022	P03690	Karapakam	9888000	10544028.0	10901647.65
5938	P03472	Chrompet	8905710	8451058.0	8217775.15
5243	P01754	Karapakam	11837500	11679182.0	11408986.60
5758	P06930	KK Nagar	8545660	9173977.0	8930851.40
2283	P09776	Velachery	15941330	16022495.0	16274440.30

Check null values¶

Replacing the Null Values with mean values of the data using Simple Imputer¶

Lets see the unique values of columns¶

Cleaning the Data¶

Changing the datatype from float to int¶

Stastical Description of Data¶

Count of Houses in different Areas in Chennai¶

How the Sales Price of Houses varies with Different Areas in Chennai ?¶

How the Square Foot of the House will Effect the Sales Price?¶

How does Sales Price Depends on the Main Road Distance From the House?¶

How the Sales Price Depends On the Build Date and Sale Date of Houses ?¶

How the Sales Price Depends On Number of Rooms ?¶

How the Sales Price Depends On Number of Bed Rooms ?¶

How the Sales Price Depends On the Number of Bath Rooms ?¶

How the Sales Price Depends On the Sale Condion of the Houses?¶

How the Sales Price Depends On the Parking Facility of the Houses?¶

How the Sales Price Depends On the Build Type of the Houses?¶

How the Sales Price Depends On the Street Type of the Houses?¶

Correlation¶

Pairwise Correlation of All Columns in the Data¶

Coverting the labels into a numeric form using Label Encoder¶

storing the Dependent Variables in X and Independent Variable in Y¶

Splitting the Data into Training set and Testing Set¶

Scaling the values to convert the int values to Machine Languages¶

Build the Regression / Regressor models¶

From the above Results, the Top 3 Models by Comparing Adjacent R Square Values are¶

	PRT_ID	AREA	INT_SQFT	DATE_SALE	DIST_MAINROAD	N_BEDROOM	N_BATHROOM	N_ROOM	SALE_COND	PARK_FACIL	DATE_BUILD	BUILDTYPE	UTILITY_AVAIL	STREET	MZZONE	QS_ROOMS	QS_BATHROOM	QS_BEDROOM	QS_OVERALL	REG_FEE	COMMIS	SALES_PRICE
0	P03210	Karapakkam	1004	04-05-2011	131	1.0	1.0	3	AbNormal	Yes	15-05-1967	Commercial	AllPub	Paved	A	4.0	3.9	4.9	4.330	380000	144400	7600000
1	P09411	Anna Nagar	1986	19-12-2006	26	2.0	1.0	5	AbNormal	No	22-12-1995	Commercial	AllPub	Gravel	RH	4.9	4.2	2.5	3.765	760122	304049	21717770
2	P01812	Adyar	909	04-02-2012	70	1.0	1.0	3	AbNormal	Yes	09-02-1992	Commercial	ELO	Gravel	RL	4.1	3.8	2.2	3.090	421094	92114	13159200
3	P05346	Velachery	1855	13-03-2010	14	3.0	2.0	5	Family	No	18-03-1988	Others	NoSewr	Paved	I	4.7	3.9	3.6	4.010	356321	77042	9630290
4	P06210	Karapakkam	1226	05-10-2009	84	1.0	1.0	3	AbNormal	Yes	13-10-1979	Others	AllPub	Gravel	C	3.0	2.5	4.1	3.290	237000	74063	7406250